Dissertations and Theses 2-2020 Predicting Pilot Misperception of Runway Excursion Risk Through Machine Learning Algorithms of Recorded Flight Data Edwin Vincent Odisho II Follow this and additional works at: https://commons.erau.edu/edt Part of the Aviation Safety and Security Commons, and the Risk Analysis Commons This Dissertation - Open Access is brought to you for free and open access by Scholarly Commons. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of Scholarly Commons. For more information, please contact
[email protected]. PREDICTING PILOT MISPERCEPTION OF RUNWAY EXCURSION RISK THROUGH MACHINE LEARNING ALGORITHMS OF RECORDED FLIGHT DATA By Edwin Vincent Odisho II A Dissertation Submitted to the College of Aviation in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Aviation Embry-Riddle Aeronautical University Daytona Beach, Florida February 2020 © 2020 Edwin Vincent Odisho II All Rights Reserved. ii ABSTRACT Researcher: Edwin Vincent Odisho II Title: PREDICTING PILOT MISPERCEPTION OF RUNWAY EXCURSION RISK THROUGH MACHINE LEARNING ALGORITHMS OF RECORDED FLIGHT DATA Institution: Embry-Riddle Aeronautical University Degree: Doctor of Philosophy in Aviation Year: 2020 The research used predictive models to determine pilot misperception of runway excursion risk associated with unstable approaches. The Federal Aviation Administration defined runway excursion as a veer-off or overrun of the runway surface. The Federal Aviation Administration also defined a stable approach as an aircraft meeting the following criteria: (a) on target approach airspeed, (b) correct attitude, (c) landing configuration, (d) nominal descent angle/rate, and (e) on a straight flight path to the runway touchdown zone.